Numerical modeling in lakes, tools and application

1. Numerical modeling in lakes, tools and application Marie-Paule Bonnet, Frédéric Guérin UMR 5563 GET IRD, CNRS, OMP, Toulouse III

2. Outlook DYLEM1D : controlling factors of Microcystis blooms and restoration process evaluation of the Villerest Reservoir (France) SYMPHONIE 2D: Controlling factors of CH4 emissions in Petit Saut Reservoir (French Guiana)

3. DYLEM1D 1D vertical model for lakes and reservoirs

4. Application to the Reservoir Villerest (Loire, France) Impounding : 1984 Mean volume: 62 Mm3 Maximum depth : 45 m Mean depth : 18 m Annual water level variation : ±15 m

5. Biogeochemical conceptual scheme Controlling factors of Microcystisaeruginosa blooms in a highly eutrophic reservoir Evaluate the restoration processes comparing two periods of study 90-92 and 97-2000

6. A large datasetavailable for modeling Nutrients(NO3, NH4, PO4, SiO2) : Everyday in the inflow Everytwoweeksduring blooms Everymonthotherwise Temperature : Every 3 hours, 11 levels in the lake Everyhour in the inflow Meteo data (every 20 mn): Solar radiation Wind speed/direction Specific relative humidity Air temperature Phytoplankton (algaespecies) : Species identification and biomasse estimation everytwoweeksduring blooms Everymonthotherwise Inflow/outflow (every 3 hours) Between the two periods of study P and N inputs are about 40 % less

7. Mixingprocessesincluded: • Dispersion induced by wind and internal seiche • advection induced by inflow/outflow • free convection • mixinginduced by surface waves The physics model Simple but requires calibration

8. The biogeochemical model A complex conceptual scheme developed step by step The phytoplankton module was developed first without considering nutrients limitation

9. 5 species Parameters for growth optimum conditions estimated from lab Phytoplankton module Buoyancy regulation for Microcystis only

10. Temperature simulation Validation Calibration year Important differences when : the 1D assumption is wrong (winter) The vertical stratification is very strong

11. Phytoplankton simulation Calibration : sensitivityanalysis and monte-carloanalysis Microcystisaeruginosae The model is able to reproduce the phytoplankton biomass at the species level mg.l-1 Calibration was required mainly because : Not all the parameters were estimated species interactions (self-shading, grazing) Cyclotella sp. mg.l-1

12. Some controlling factors of Microcystis blooms buoyancyregulation Beside optimum conditions in terms of temperature, buoyancy regulation ability combined with a strong vertical stratification is an important feature for explaining Microcystis dominance in the reservoir Reference Vertical stratification

13. Evaluation of the Restoration process Despite significant P-PO4 load reduction, Microcystis remains dominant

14. Evaluation of the Restoration process

15. Conclusions • Model strength : • Working at the planktonic species level which enables to tackle some of the controlling factors of the planktonic succession and of Microcystis dominance • Relatively good “predictive capacities” which enable following the reservoir evolution in response to nutrients inputs reduction • Model weakness : • 1D assumption is not always valid and influences biogeochemical results • Large calibration effort was required to work at the species level despite laboratory estimation of main parameters

16. SYMPHONIE 2D applied to reservoir Modeling CH4 and CO2 emissions from a tropical freshwater reservoir: The Petit Saut Reservoir F. Guérin, MP Bonnet, G. Abril, R. Delmas

17. Methodology Site: Petit Saut Reservoir in French Guiana, filled in 1994 The most documented tropical reservoir (10 years of monitoring) Identification of the main processes controlling emissions Determination of the kinetics in the lab/field Process-based model

18. SYMPHONIE 2D Physical model • Mean daily atmospheric forcing • Wind speed • Air temperature • Relative humidity • Air pressure • Solar radiation • IR Radiation • Daily water inflow (including rainfall) and outflow • Constant temperature for water entering the Reservoir ≈ 100 km ≈ 3.5 km3 No model for the river downstream Run must be started with the reservoir at full operating level

19. Biogeochemical model Source and sink terms of the biogeochemical model vertical turbulent diffusion Advection Diffusive fluxes No model for bubblingNo module for OM cycling in the water column

20. CH4and CO2 production Production by flooded soil and biomass • Incubation in anaerobic condition during one year of • ≠Soils & • ≠ Plant material from the forest surrounding the reservoir Guérin et al., submitted Production CH4 and CO2 -> PLANT > SOIL PLANTS ≈ 40-50% CH4 SOILS < 30% CH4

21. CH4and CO2 production Production by flooded soil and biomass Guérin et al., 2008 Emissions from Abril et al., 2005 Oxidation = Production - Emission Year 2003: CH4 Oxidation = 85% of CH4 production ( ≈ 50GgC y-1)

22. CH4oxidation • Water from • different stations in the lake • Different depths • In the epilimnion • At the oxycline • In the river below the dam • Incubation of water • In aerobic conditions • In the dark • At different CH4 concentrations Guérin and Abril, 2007 Specific oxidation rate VCH4= 0.11±0.01 h-1

23. Diffusive fluxes Fdiff =kGHG, T(Pwater – Patm) Rain effect Wind effect Guérin et al., 2007 k at low wind speed ≈ 50% higher than in temperate/cold environment Rainfall contributes to 25% of diffusive fluxes

24. Biogeochemical modeling In contrast, very simple scheme for other processes Respiration and Photosynthesis Photosynthesis (After Vaquer et al., 1997 & Collos et al., 2001) Autotrophic respiration Heterotrophic respiration (BOD determined after Dumestre (1998) and HYDRECO unpublished data)

25. Results January Temp O2 CO2 CH4 June July December

26. Results Dry Season OM cycling in the reservoir has a significant impact on Conc.

27. Results Atmospheric fluxes Degassing Diffusive fluxes CO2 CO2 CH4 CH4 Good reproduction of vertical profiles of conc. is crucial for degassing

28. Conclusion • Strength of model • Simple formulation • Kinetics determined on site -> No calibration required • Models are efficient tools for the computation of mass balance since it integrates: • Biogeochemical processes • Hydrodynamics • The approach enables to identify lack in the scheme • A module for OM (Allochthonous and Autochthonous) cycling in the water column of reservoirs must be included